This is due to the rapid expansion of computing (Cutri et al., 2013), had many technical challenges and resources and sensor technology in the last four required intensive astronomy expertise, experience, and labor decades that has driven equally rapid expansions in the to overcome (Eisenhardt et al., 2012, for example). A quantity of data to analyze. Astronomy, in particular, necessary first step in that process, though, is to classify has seen a proliferation of large scale imaging and spectroscopic the sources so that we can prioritize which sources might surveys that have billions of sources in them-- be interesting, and which are examples of already known surveys like: the Sloan Digital Sky Survey (SDSS, York sources. Because these sources are rare it is usually easier et al., 2000), the 2-Micron All Sky Survey (2MASS, Skrutskie to use a supervised machine learning algorithm, one that et al., 2006), the Wide-field Infrared Survey Explorer is tuned using sources with known classifications, than it (WISE, Wright et al., 2010), the Gaia satellite's survey is to use an unsupervised one. The reason should be obvious: (Gaia Collaboration et al., 2016), the Panoramic Survey subgroups of the common known source types are Telescope and Rapid Response System (Pan-STARRS) likely to outnumber the rare new ones, meaning a naive surveys (Chambers et al., 2016), the Dark Energy Spectroscopic unsupervised machine learning algorithm could need a lot Instrument (DESI) surveys (Dey et al., 2019), the of complexity before it actually finds the rare class. UKIRT Infrared Deep Sky Surveys (UKIDSS, Lawrence et al., 2007), and the Galaxy Evolution Explorer (GALEX) Supervised learning also has drawbacks when used for surveys (Martin et al., 2005).

Frequently asked questions What are the benefits of using CausalNex? When should you consider using CausalNex?

In order to learn efficiently in a complex world with multiple, sometimes rapidly changing objectives, both animals and machines must leverage information obtained from past experience. This is a challenging task, as processing and storing all relevant information is computationally infeasible. How can an intelligent agent address this problem? We hypothesize that one route may lie in the dual process theory of cognition, a longstanding framework in cognitive psychology first introduced by William James (James, 1890) which lies at the heart of many dichotomies in both cognitive science and machine learning. Examples include goal-directed versus habitual behavior (Graybiel, 2008), model-based versus model-free reinforcement learning (Daw et al., 2011; Sutton and Barto, 2018), and "System 1" versus "System 2" thinking (Kahneman, 2011).

This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal and offers insight into the strengths and weaknesses of several widely utilized prediction models for this application.

In multi-label learning, a particular case of multi-task learning where a single data point is associated with multiple target labels, it was widely assumed in the literature that, to obtain best accuracy, the dependence among the labels should be explicitly modeled. This premise led to a proliferation of methods offering techniques to learn and predict labels together, for example where the prediction for one label influences predictions for other labels. Even though it is now acknowledged that in many contexts a model of dependence is not required for optimal performance, such models continue to outperform independent models in some of those very contexts, suggesting alternative explanations for their performance beyond label dependence, which the literature is only recently beginning to unravel. Leveraging and extending recent discoveries, we turn the original premise of multi-label learning on its head, and approach the problem of joint-modeling specifically under the absence of any measurable dependence among task labels; for example, when task labels come from separate problem domains. We shift insights from this study towards building an approach for transfer learning that challenges the long-held assumption that transferability of tasks comes from measurements of similarity between the source and target domains or models. This allows us to design and test a method for transfer learning, which is model driven rather than purely data driven, and furthermore it is black box and model-agnostic (any base model class can be considered). We show that essentially we can create task-dependence based on source-model capacity. The results we obtain have important implications and provide clear directions for future work, both in the areas of multi-label and transfer learning.

Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes.

A practical solution to these problems has been using hidden Markov models (HMMs), which (i) can A particular challenge for disease progression be trained using small datasets, (ii) can handle missing data modeling is the heterogeneity of a disease and in a principled approach and (iii) are interpretable models, its manifestations in the patients. Existing approaches e.g., it is possible to relate inferred latent states to particular often assume the presence of a single symptoms. Most existing HMMs (Jackson et al., 2003; disease progression characteristics which is unlikely Sukkar et al., 2012; Guihenneuc-Jouyaux et al., 2000; Wang for neurodegenerative disorders such as et al., 2014; Sun et al., 2019; Severson et al., 2020; 2021), Parkinson' disease. In this paper, we propose however, assume that each patient follows the same latent a hierarchical time-series model that can discover state transition dynamics, ignoring the heterogeneity in the multiple disease progression dynamics. The proposed disease progression dynamics.

Training and testing supervised object detection models require a large collection of images with ground truth labels. Labels define object classes in the image, as well as their locations, shape, and possibly other information such as pose. The labeling process has proven extremely time consuming, even with the presence of manpower. We introduce a novel labeling tool for 2D images as well as 3D triangular meshes: 3D Labeling Tool (3DLT). This is a standalone, feature-heavy and cross-platform software that does not require installation and can run on Windows, macOS and Linux-based distributions. Instead of labeling the same object on every image separately like current tools, we use depth information to reconstruct a triangular mesh from said images and label the object only once on the aforementioned mesh. We use registration to simplify 3D labeling, outlier detection to improve 2D bounding box calculation and surface reconstruction to expand labeling possibility to large point clouds. Our tool is tested against state of the art methods and it greatly surpasses them in terms of speed while preserving accuracy and ease of use.

Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepancy between the map and the observed environment caused by such changes, we exploit human-readable localization cues to assist localization. These cues are readily available in most facilities and can be detected using RGB camera images by utilizing text spotting. We integrate these cues into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. By this, we provide a robust localization solution for environments with structural changes and dynamics by humans walking. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We release an open source implementation of our approach (upon paper acceptance), which uses off-the-shelf text spotting, written in C++ with a ROS wrapper.

As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have the ability to move through intersections in a faster and safer manner, through effective Vehicle-to-Everything (V2X) communication and global observation. Autonomous intersection management is a key path to efficient crossing at intersections, which reduces unnecessary slowdowns and stops through adaptive decision process of each CAV, enabling fuller utilization of the intersection space. Distributed reinforcement learning (DRL) offers a flexible, end-to-end model for AIM, adapting for many intersection scenarios. While DRL is prone to collisions as the actions of multiple sides in the complicated interactions are sampled from a generic policy, restricting the application of DRL in realistic scenario. To address this, we propose a hierarchical RL framework where models at different levels vary in receptive scope, action step length, and feedback period of reward. The upper layer model accelerate CAVs to prevent them from being clashed, while the lower layer model adjust the trends from upper layer model to avoid the change of mobile state causing new conflicts. And the real action of CAV at each step is co-determined by the trends from both levels, forming a real-time balance in the adversarial process. The proposed model is proven effective in the experiment undertaken in a complicated intersection with 4 branches and 4 lanes each branch, and show better performance compared with baselines.